contourforest Contour-Enhanced Forest Plots for Meta-Analysis This R package provides functions to create contour-enhanced forest plots for meta-analysis of **continuous** and **binary** outcomes. It integrates with `metafor` and `ggplot2` and allows customization of colors, labels, prediction intervals, and subgroup analyses. A detailed guide for using the `contourforest` package. 📖 Read the Full GuideFor step-by-step instructions and examples, visit the RPubs guide: https://rpubs.com/umarhussain/1382129 (https://rpubs.com/umarhussain/1382129) ## 🚀 How to Run on Different Types of DatasetsThe guide covers running `contourforest` on various dataset formats and scenarios. Follow the instructions in the RPubs tutorial to get started quickly. Installation of contourforest ```r# Install remotes if not already installed# install.packages('remotes') remotes::install_github('umarhussain-git/contourforest')``` ## Functions bcg - BCG Vaccine Trials Datasetdat1- Hypothetical dataset: GAD-7 Anxiety Scores by Socioeconomic Statusforest_bin() - Contour-Enhanced Binary Outcome Forest Plotforest_bin_subgroup() - Subgroup Forest Plot for Binary Outcome Meta-analysisforest_cont() - Contour-enhanced Forest Plot for Continuous Outcomesforest_cont_subgroup() - Forest Plot for Subgroup Meta-Analysis## Example Datasetsdat – Hypothetical dataset: Depression by Socioeconomic Status dat1 – Hypothetical dataset: GAD-7 Anxiety Scores by Socioeconomic Statuslibrary(contourForest) # Example 1: Binary Outcomes#` Load example dataset data(bcg()) #' Generate a binary forest plot (Odds Ratio) ```rforest_bin( dat = bcg(), measure = "OR", xlab = "Odds Ratio", title = "BCG Vaccine Meta-analysis", tlim = c(0, 2.3), contour_left_min = c(0,0.3,0.5,0.7), contour_left_max = c(0.3,0.5,0.7,1), contour_right_min = c(1,1.2,1.5,1.8), contour_right_max = c(1.2,1.5,1.8,2.5))``` library(contourforest) #' Load example dataset data(dat1) #' View first rows head(dat1)### Example 2: Continuous Outcomes#' Generate a continuous forest plot (Standardized Mean Difference or Mean Difference)```rforest_cont( dat1, measure = "MD", xlab = "Mean Difference", study_x = -9, sort = "effect", hetero_x = -12, treatment_x = -7, control_x = -5, effect_x = 5.5, weight_x = 10, PredInt_x = 7) forest_cont( dat1, measure = "SMD", xlab = "Standardized Mean Difference", hetero_x = -9.9, study_x = -7, sort = "effect", treatment_x = -5, control_x = -3, effect_x = 2.5, weight_x = 4, PredInt_x = 4)forest_cont(dat1,study.col = "darkgreen",CI.col = "black",diamond.col = "red",Pred.Inter.col = "black",measure = "SMD",sort = "effect",xlab = "Standardized Mean Difference",contour_fill = c("gray90","gray70","gray50", "gray30"),hetero_x = -9.9,study_x = -7,square.size = 9,treatment_x = -5,control_x = -3.2,text_size = 4,effect_x = 2.5,weight_x = 5.8,PredInt_x = 3)``` ### Example 3: Binary Outcomes with Subgroupslibrary(contourForest) #, Generate a binary forest plot with subgroups```rforest_bin_subgroup(bcg(), measure = 'OR')``` ### Example 4: Continuous Outcomes with Subgroupslibrary(contourforest) #` Generate a continuous forest plot with subgroups ```r# Using the built-in dataset dat1forest_cont_subgroup(dat1)``` ## Features Contour-enhanced visualization to highlight statistical significance Supports Odds Ratios (OR), Risk Ratios (RR), Mean Differences (MD), and Standardized Mean Differences (SMD) Optional prediction intervals Customizable colors, labels, and annotation positions Subgroup analysis with pooled effects forest_cont() – Forest plot for continuous outcomes forest_cont_subgroup() – Forest plot for continuous outcomes with subgroups# Maintainer ## Umar Hussain drumarhussain@gmail.com Assistant professor, Orthodontics Saidu college of Dentistry